The purpose of SISR is to reconstruct a high-resolution (HR) image from its low-resolution (LR) counterpart. Before the deep learning age, researchers have used exemplar-based or sparse coding methods for SISR. Exemplar-based methods learn mapping functions from external LR and HR exemplar pairs [1]}, [2]}, [3]}. Sparse coding methods [4]} are representative for external exemplar-based SR methods. For example, the method of Yang et al. [4]} builds a dictionary with LR patches and the corresponding HR patches.
